| 安徽省2021年梅雨期降水预报检验分析 |
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| 引用本文： | 周胜男,王东勇,冯颖,柳春,朱珠,刘倪,郑淋淋.安徽省2021年梅雨期降水预报检验分析[J].新疆气象,2023,17(6). |
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| 作者姓名： | 周胜男 王东勇 冯颖 柳春 朱珠 刘倪 郑淋淋 |
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| 作者单位： | 安徽省气象台;中国科学技术大学地球和空间科学学院,安徽省气象台,安徽省气象台,安徽省气象台,安徽省气象台,安徽省气象台,安徽省气象台 |
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| 基金项目： | 长江流域气象开放基金项目(CJLY2022Y04)；安徽省重点研究开发项目(2022h11020002)；安徽省气象局创新发展专项(CXB202102)；中国气象局创新发展专项(CXFZ2021Z033)；中国气象局创新发展专项(CXFZ2021Z007) |
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| 摘 要： | 检验梅雨期降水的预报效果，对于提升梅雨期降水预报能力、减少梅雨期降水带来的人员伤亡和经济财产损失有着重要的意义。文章对安徽省2021年梅雨期（6月10日—7月10日）六个客观模式和一个主观订正预报产品进行了检验分析，其中包含了三个区域模式数值预报（中国气象局中尺度天气数值预报系统（简称CMA-MESO）、中国气象局上海数值预报模式系统（简称CMA-SH9）、安徽WRF）、三个全球模式数值预报（中国气象局全球同化预报系统（简称CMA-GFS）、欧洲中期天气预报中心确定性预报模式（简称ECMWF）、美国国家环境预报中心全球预报系统（简称NCEP-GFS））和安徽智能网格主观订正预报的降水产品，进行了检验分析，结果表明：传统检验中安徽智能网格和区域模式对晴雨准确率的预报效果优于全球模式，又以CMA-MESO最优；在暴雨及以上量级的强降水预报中，传统检验表明安徽智能网格预报的得分最高（23.83），ECMWF模式则是客观模式预报中效果最好的（20.12），CMA-SH9次之（19.34）；通过对除安徽智能网格以外的各个客观数值模式进行的MODE空间检验可知，不同数值模式间暴雨预报误差原因不尽相同，ECMWF与各区域数值模式主要是由雨区位置的预报偏差，尤其是纬度偏差导致的，NCEP-GFS全球模式对降水强度和雨区面积的预报偏弱偏小比较明显，CMA-GFS在强降水方面的预报可参考性较差；各个主客观预报暴雨及以上量级预报，整体表现出较明显的日变化特征，在午夜前后、上午时段TS评分较高，而午后到傍晚评分较低，这个现象或许是梅雨期的午后降水多以地表太阳加热引起的短历时热对流降水为主造成的。
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| 关 键 词： | 降水检验 MODE方法 梅雨 数值预报模式 |
| 收稿时间： | 2022/5/31 0:00:00 |
| 修稿时间： | 2022/11/24 0:00:00 |
Verification and Analysis of Precipitation Forecast during Meiyu Period of 2021 in Anhui Province |
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| zhoushengnan,and.Verification and Analysis of Precipitation Forecast during Meiyu Period of 2021 in Anhui Province[J].Bimonthly of Xinjiang Meteorology,2023,17(6). |
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| Authors: | zhoushengnan and |
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| Abstract: | Verifying forecast effect and characteristics of precipitation has contributed to improve the precipitation forecast ability and reduce the casualties and economic property losses caused by heavy rainfall during the Meiyu period. This paper focuses on comparative analysis of six different objective models and one subjective revised forecast. Three regional models (CMA-MESO, CMA-SH9, Anhui WRF), three global models (CMA-GFS, ECMWF, NCEP-GFS) and the Anhui Intelligent Grid (AIG) subjective forecast are vertified during the Meiyu period of 2021 (from June 10 to July 10) in Anhui Province. The results show that: The forecast effect of AIG forecast and regional models on the accuracy of sunny and rain are better than the global models, in which CMA-MESO is the best performer. In terms of precipitation forecast of rainstorm magnitude and above, the traditional verification method shows that AIG forecast has the highest score (23.83), while the ECMWF model is the best objective model forecast (20.12), followed by CMA-SH9 (19.34). The MODE (Method for Object-based Diagnostic Evaluation) spatial examination of each objective numerical model except for the AIG shows that the causes of rainstorm forecast errors vary among different numerical models. ECMWF and regional numerical models are mainly caused by the prediction deviation of rain area location, especially the latitude deviation. The prediction of precipitation intensity and rain area by NCEP-GFS is weak and small, while the prediction of CMA-GFS in heavy precipitation is poor. The subjective and objective forecasts of rainstorm magnitude and above menifest an diurnal variation. The TS (Threat Score) before midnight and in the forenoon are higher than that in the afternoon and evening, this phenomenon may be caused by the fact that afternoon precipitation during the Meiyu period is mostly dominated by short-duration thermal convective precipitation caused by solar heating at the surface. |
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| Keywords: | Precipitation verification MODE Meiyu period Numerical prediction model |
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